Outline

Motivation

Experimental PV installation spots

PV in highway turnoffs: The idea

Scaling across Germany

Satellite imagery of two highway turnoffs in Brandenburg

When is a turnoff suitable for PV?

https://solarbusinesshub.com/2015/04/10/bike-path-on-koreas-highway-covered-with-solar-panels/

Xinhua News Agency via Getty Images

Insights from pilot project “Lustnauer Ohren”

Derivation of the project’s objective

Conclusions for contextual focus of our project

Driving factors for one-off costs due to construction and operating costs:

Note: The environmental assessment determines one-off costs and long-term profitability of the PV.

Conclusions for our project from insights (pilot project)

Driving factors for one-off costs due to construction and operating costs:

Economic Model (Felix)

How is score calculated?

Data (Felix)

Potentially cover the following: * Data acquisition

Land cover classification

Figure credit: Choi, U. (2021, March 19) Semantic Segmentation (FCN, U-Net, DeepLab V3+).

Training data - Postdam

Potsdam 2D Semantic Segmentation dataset for the German city Potsdam, provided by the International Society for Photogrammetry and Remote Sensing (ISPRS)

Label Color
1.) Impervious surfaces white
2.) Building blue
3.) Low vegetation light-blue
4.) Tree green
5.) Car yellow
6.) Clutter/background red

Data Source: 2D Semantic Labeling Contest - Potsdam. Available online: https://www.isprs.org/education/benchmarks/UrbanSemLab/default.aspx (accessed on 14 December 2022).

Training data - Postdam

Advantages:

Disadvantages:

Training data - Postdam

Color Labels: White - Impervious surfaces; Light-blue - Low vegetation; Green - Tree

Training data - LoveDA (regions of Nanjing, Changzhou and Wuhan / China)

Land-cOVEr Domain Adaptive semantic segmentation (LoveDA) dataset + High spatial resolution land-cover mapping (spatial resolution of 30 cm) + Source Google Earth platform

Label Color
1.) Background white
2.) Building blue
3.) Road light-blue
4.) Water green
5.) Barren yellow
6.) Forest red
7.) Agriculture red
0.) Unknown red

Training data - LoveDA (regions of Nanjing, Changzhou and Wuhan / China)

Advantages * Dataset differentiates between rural and urban images + More rural scenes, but quite balanced dataset (2713 urban, 3274 rural) * Dataset created with the intention of achieving model transferability + “[U]rban and rural scenes can show completely different geographical landscapes, and the inadequate [generalization] of these algorithms hinders city-level or national-level mapping” (Wang et al., 2022, p. 1) + Dataset encounters for: 1) multi-scale objects; 2) complex background samples; 3) inconsistent class distributions

Disadvantages * Lower resolution than images from Brandenburg (30 cm vs. 20 cm, respectively) * Satellite images (vs. aerial images from Brandenburg) * [Less detailed labeling compared to Potsdam dataset] + Useful in training for the recognition of larger contiguous areas, but not individual trees

Semantic Segmentation - UNet

Model Advantages Disadv.
UNet - widely used basic segmentation model - training from scratch
- feedback from interim presentation - model overfits to training data
- good starting point - labelled data not specific for our task

Semantic Segmentation - ResNet50 + BigEarthNet (Sumbul et al., 2019)

Semantic Segmentation - ResNet50 + BigEarthNet (Sumbul et al., 2019)

Model Advantages Disadv.
ResNet50 - pre-trained weights - trained for classification task
+ BigEarthNet - seasonal satellite images - inferior predictions
- big dataset on European countries

Semantic Segmentation - ResNet101 + COCO (Lin et al., 2014)

Model Advantages Disadv.
ResNet101 - pre-trained weights - COCO dataset
+ COCO - segmentation task - trained for object segmentation
- superior predictions after few training iterations
- least overfitting
- complexity of model seems to fit task

Semantic Segmentation - ResNet101 + COCO - Results

Color Labels: White - Impervious surfaces; Red - Road; Green - Trees

Semantic Segmentation - ResNet101 + COCO - Results

Color Labels: White - Impervious surfaces; Red - Road; Green - Trees; Yellow - Agricultur

Semantic Segmentation - ResNet101 + COCO - Results

Color Labels: White - Impervious surfaces; Red - Road; Green - Trees; Pink - Building

Presentation of the result - Dashboard (Jan)

References